Detecting Multivariate Gene Interactions in RNA-Seq Data Using Optimal Bayesian Classification.

Differential gene expression testing is an analysis commonly applied to RNA-Seq data. These statistical tests identify genes that are significantly different across phenotypes. We extend this testing paradigm to multivariate gene interactions from a classification perspective with the goal to detect novel gene interactions for the phenotypes of interest. This is achieved through our novel computational framework comprised of a hierarchical statistical model of the RNA-Seq processing pipeline and the corresponding optimal Bayesian classifier. Through Markov Chain Monte Carlo sampling and Monte Carlo integration, we compute quantities where no analytical formulation exists. The performance is then illustrated on an expression dataset from a dietary intervention study where we identify gene pairs that have low classification error yet were not identified as differentially expressed. Additionally, we have released the software package to perform OBC classification on RNA-Seq data under an open source license and is available at http://bit.ly/obc_package.

Assessment of histone tail modifications and transcriptional profiling during colon cancer progression reveals a global decrease in H3K4me3 activity.

During colon cancer, epigenetic alterations contribute to the dysregulation of major cellular functions and signaling pathways. Modifications in chromatin signatures such as H3K4me3 and H3K9ac, which are associated with transcriptionally active genes, can lead to genomic instability and perturb the expression of gene sets associated with oncogenic processes. In order to further elucidate early pre-tumorigenic epigenetic molecular events driving CRC, we integrated diverse, genome-wide, epigenetic inputs (by high throughput sequencing of RNA, H3K4me3, and H3K9ac) and compared differentially expressed transcripts (DE) and enriched regions (DER) in an in-vivo rat colon cancer progression model. Carcinogen (AOM) effects were detected genome-wide at the RNA (116 DE genes), K9ac (49 DERs including 24 genes) and K4me3 (7678 DERs including 3792 genes) level. RNA-seq differential expression and pathway analysis indicated that interferon-associated innate immune responses were impacted by AOM exposure. Despite extensive associations between K4me3 DERs and colon tumorigenesis (1210 genes were linked to colorectal carcinoma) including FOXO3, GNAI2, H2AFX, MSH2, NR3C1, PDCD4 and VEGFA, these changes were not reflected at the RNA gene expression level during early cancer progression. Collectively, our results indicate that carcinogen-induced changes in gene K4me3 DERs are harbingers of future transcriptional events, which drive malignant transformation of the colon.